{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3",
   "language": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "love\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "print(\"love\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   0  1   2   3\n",
       "0  0  1   2   3\n",
       "1  4  5   6   7\n",
       "2  8  9  10  11"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4</td>\n      <td>5</td>\n      <td>6</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>9</td>\n      <td>10</td>\n      <td>11</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "s=pd.DataFrame(np.arange(12).reshape(-1,4))\n",
    "s"
   ]
  },
  {
   "source": [
    "![](https://mmbiz.qpic.cn/mmbiz_png/yjFUicxiaClgWcr3FpZjxq3cZLrVhQf7DAgyKyuZxEnHthGG5vovu9hYJp3HgbXIqywKFP2HfLWaajBN9lFyRqQA/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   0  1     2   3\n",
       "0  0  1   2.0   3\n",
       "1  4  5   NaN   7\n",
       "2  8  9  10.0  11"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>9</td>\n      <td>10.0</td>\n      <td>11</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "s.loc[1,2]=np.nan\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "TypeError",
     "evalue": "'numpy.int32' object is not callable",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-adac875d1e14>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# s.size()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m# s.count()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'numpy.int32' object is not callable"
     ]
    }
   ],
   "source": [
    "# s.min()\n",
    "# s.max()\n",
    "# s.sum(axis=1)\n",
    "# s.mean()\n",
    "# s.size()\n",
    "# s.count()\n",
    "s.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   0  1     2   3\n",
       "0  0  1   2.0   3\n",
       "1  4  5   NaN   7\n",
       "2  8  9  10.0  11"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>9</td>\n      <td>10.0</td>\n      <td>11</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0    4.0\n",
       "1    5.0\n",
       "2    6.0\n",
       "3    7.0\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "s.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0    16.0\n",
       "1    16.0\n",
       "2    32.0\n",
       "3    16.0\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "source": [
    "s.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0    4.000000\n",
       "1    4.000000\n",
       "2    5.656854\n",
       "3    4.000000\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "s.std()"
   ]
  },
  {
   "source": [
    "### *概率统计\n",
    "![概率论](https://mmbiz.qpic.cn/mmbiz_png/yjFUicxiaClgWcr3FpZjxq3cZLrVhQf7DAgBPMceof4oMcfAKvDJC8BUkdOhqClK8licic0vTIqTwozqBEtCdnogRg/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "count    1000.000000\nmean        1.940019\nstd         3.094528\nmin       -11.075570\n25%        -0.120911\n50%         1.947553\n75%         4.019022\nmax        12.431103\ndtype: float64\nA    17.585652\nB    16.801092\nC    15.877066\ndtype: float64\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "A    17.773520\n",
       "B    16.979347\n",
       "C    15.539425\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 45
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "x = pd.Series(np.random.normal(2,3,1000))\n",
    "# print(x)\n",
    "y = 3*x + 10 + pd.Series(np.random.normal(1,2,1000))\n",
    "\n",
    "# 计算x与y的相关系数\n",
    "# print('计算x与y的相关系数')\n",
    "# print(x.corr(y))\n",
    "# print('计算y的偏度')\n",
    "# # 计算y的偏度\n",
    "# print(y.skew())\n",
    "# print('计算y的统计描述值')\n",
    "# 计算y的统计描述值\n",
    "print(x.describe())\n",
    "\n",
    "z = pd.Series(['A','B','C']).sample(n = 1000, replace = True)\n",
    "# print(z)\n",
    "# 重新修改z的行索引\n",
    "z.index = range(1000)\n",
    "# 按照z分组，统计y的组内平均值\n",
    "print(y.groupby(by = z).aggregate(np.mean))###\n",
    "y.groupby(by = z).aggregate(np.median)###"
   ]
  },
  {
   "source": [
    "![](https://mmbiz.qpic.cn/mmbiz_png/yjFUicxiaClgWcr3FpZjxq3cZLrVhQf7DAA60egx96aJiaPcyibYwdGmjzf5kQWkrqPnk5d4mV8yZ5MbHgXkgCQupQ/640?wx_fmt=jpeg&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "A    353\nB    341\nC    306\ndtype: int64\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "C    306\n",
       "B    341\n",
       "A    353\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 59
    }
   ],
   "source": [
    "# 统计z中个元素的频次\n",
    "print(z.value_counts())\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "df=pd.Series(Counter(z),index=Counter(z).keys())\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0     1\n1     5\n2    10\n3    15\n4    25\n5    30\ndtype: int64\n0    0.011628\n1    0.069767\n2    0.186047\n3    0.360465\n4    0.651163\n5    1.000000\ndtype: float64\n"
     ]
    }
   ],
   "source": [
    "a = pd.Series([1,5,10,15,25,30])\n",
    "# 计算a中各元素的累计百分比\n",
    "print(a)\n",
    "print(a.cumsum() / a.cumsum()[a.size - 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0     1\n",
       "1     6\n",
       "2    16\n",
       "3    31\n",
       "4    56\n",
       "5    86\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 62
    }
   ],
   "source": [
    "a.cumsum()#分布函数"
   ]
  },
  {
   "source": [
    "![](https://mmbiz.qpic.cn/mmbiz_png/yjFUicxiaClgWcr3FpZjxq3cZLrVhQf7DArRibfkDiaibibjqvlks18rgKYAcgV97ibibnBKMcU9DQS9Fax2lhZfhichkxQ/640?wx_fmt=jpeg&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0    10.0\n1    13.0\n2     NaN\n3    17.0\n4    28.0\n5    19.0\n6    33.0\n7     NaN\n8    27.0\ndtype: float64\nTrue\n0    10.0\n1    13.0\n2    21.0\n3    17.0\n4    28.0\n5    19.0\n6    33.0\n7    21.0\n8    27.0\ndtype: float64\n0    10.0\n1    13.0\n2    13.0\n3    17.0\n4    28.0\n5    19.0\n6    33.0\n7    33.0\n8    27.0\ndtype: float64\n"
     ]
    }
   ],
   "source": [
    "x = pd.Series([10,13,np.nan,17,28,19,33,np.nan,27])\n",
    "print(x)\n",
    "#检验序列中是否存在缺失值\n",
    "print(x.hasnans)\n",
    "\n",
    "# 将缺失值填充为平均值\n",
    "print(x.fillna(value = x.mean()))\n",
    "\n",
    "# 前向填充缺失值\n",
    "print(x.ffill())"
   ]
  },
  {
   "source": [
    "![](https://mmbiz.qpic.cn/mmbiz_png/yjFUicxiaClgWcr3FpZjxq3cZLrVhQf7DA760xN6rkCBnSA5orMO7QAFssG7ooBMOATib83g7weP52eSoqibEHiacEg/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0    12500\n1     8000\n2     8500\n3    15000\n4     9000\ndtype: int32\n(array([0, 1, 1, 1, 0, 1], dtype=int64), Index(['男', '女'], dtype='object'))\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0    158.32\n",
       "1    104.73\n",
       "2     68.38\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 66
    }
   ],
   "source": [
    "income = pd.Series(['12500元','8000元','8500元','15000元','9000元'])\n",
    "# 将收入转换为整型\n",
    "print(income.str[:-1].astype(int))\n",
    "\n",
    "gender = pd.Series(['男','女','女','女','男','女'])\n",
    "# 性别因子化处理\n",
    "print(gender.factorize())\n",
    "\n",
    "house = pd.Series(['大宁金茂府 | 3室2厅 | 158.32平米 | 南 | 精装',\n",
    "                   '昌里花园 | 2室2厅 | 104.73平米 | 南 | 精装',\n",
    "                   '纺大小区 | 3室1厅 | 68.38平米 | 南 | 简装'])\n",
    "# 取出二手房的面积，并转换为浮点型\n",
    "house.str.split('|').str[2].str.strip().str[:-2].astype(float)\n",
    "#strip()连接函数"
   ]
  },
  {
   "source": [
    "![](https://mmbiz.qpic.cn/mmbiz_png/yjFUicxiaClgWcr3FpZjxq3cZLrVhQf7DAST03oGaAwxz5yFCwXlnTKIy7JW6J0avN3KakX8cH2QGtRr6kaXyJiaA/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "4    18\n5    19\n7    17\n8    19\ndtype: int32\n0    13\n1    16\n2    15\n3    14\n9    16\ndtype: int32\n5    19\n8    19\n4    18\ndtype: int32\n0    ID:1 name:张三 age:24 income:13500\n1    ID:2 name:李四 age:27 income:25000\n2     ID:3 name:王二 age:21 income:8000\ndtype: object\n0    24\n1    27\n2    21\ndtype: int32\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1234)\n",
    "x = pd.Series(np.random.randint(10,20,10))\n",
    "\n",
    "# 筛选出16以上的元素\n",
    "print(x.loc[x > 16])\n",
    "\n",
    "# print(x.compress(x > 16))####压缩？？？\n",
    "\n",
    "# 筛选出13~16之间的元素\n",
    "print(x[x.between(13,16)])\n",
    "\n",
    "# 取出最大的三个元素\n",
    "print(x.nlargest(3))\n",
    "\n",
    "y = pd.Series(['ID:1 name:张三 age:24 income:13500',\n",
    "               'ID:2 name:李四 age:27 income:25000',\n",
    "               'ID:3 name:王二 age:21 income:8000'])\n",
    "# y=pd.DataFrame(['ID:1 name:张三 age:24 income:13500',\n",
    "#                 'ID:2 name:李四 age:27 income:25000',\n",
    "#                 'ID:3 name:王二 age:21 income:8000'])\n",
    "print(y)\n",
    "# 取出年龄，并转换为整数\n",
    "print(y.str.findall('age:(\\d+)').str[0].astype(int))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0    [1, 24, 13500]\n",
       "1    [2, 27, 25000]\n",
       "2     [3, 21, 8000]\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 106
    }
   ],
   "source": [
    "y=pd.DataFrame(['ID:1 name:张三 age:24 income:13500',\n",
    "                'ID:2 name:李四 age:27 income:25000',\n",
    "                'ID:3 name:王二 age:21 income:8000'])\n",
    "y = pd.Series(['ID:1 name:张三 age:24 income:13500',\n",
    "                'ID:2 name:李四 age:27 income:25000',\n",
    "                'ID:3 name:王二 age:21 income:8000'])\n",
    "import re\n",
    "ex = '\\d+'####  经典：            .*?\n",
    "y.str.findall(ex)\n",
    "y.str.findall(ex,re.S)"
   ]
  },
  {
   "source": [
    "![](https://mmbiz.qpic.cn/mmbiz_png/yjFUicxiaClgWcr3FpZjxq3cZLrVhQf7DAV4AASQVpgF73znfZb7jHJjcrZeOb3Njibpk6AJ3VDxhx8Hm5NxUpUtQ/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0     1280.0000\n",
       "1    67000.0000\n",
       "2     2488.0000\n",
       "3     1892.0000\n",
       "4        0.9877\n",
       "5     9877.0000\n",
       "6    12000.0000\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 68
    }
   ],
   "source": [
    "np.random.seed(123)\n",
    "import matplotlib.pyplot as plt\n",
    "x = pd.Series(np.random.normal(10,3,1000))\n",
    "# 绘制x直方图\n",
    "x.hist()\n",
    "# 显示图形\n",
    "plt.show()\n",
    "\n",
    "# 绘制x的箱线图\n",
    "x.plot(kind='box')\n",
    "plt.show()\n",
    "\n",
    "installs = pd.Series(['1280万','6.7亿','2488万','1892万','9877','9877万','1.2亿'])\n",
    "# 将安装量统一更改为“万”的单位\n",
    "def transform(x):\n",
    "    if x.find('亿') != -1:\n",
    "        res = float(x[:-1])*10000\n",
    "    elif x.find('万') != -1:\n",
    "        res = float(x[:-1])\n",
    "    else:\n",
    "        res = float(x)/10000\n",
    "    return res\n",
    "installs.apply(transform)"
   ]
  },
  {
   "source": [
    "![](https://mmbiz.qpic.cn/mmbiz_png/yjFUicxiaClgWcr3FpZjxq3cZLrVhQf7DAyJficxdQXIFWwOWckmJa5iaFXqDLJO4PGt2OqibH4NGyBDib2M3rzERMgw/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1)\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "![](https://mmbiz.qpic.cn/mmbiz_png/yjFUicxiaClgWcr3FpZjxq3cZLrVhQf7DATiaKyxnmZz5vQBugvpBiabIRqWoShYEviaKwibYtJlLicMRmjXldibANHpXg/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "![](https://mmbiz.qpic.cn/mmbiz_png/yjFUicxiaClgWcr3FpZjxq3cZLrVhQf7DAnPCZIXKMlF5e0pg8Jwd3WfZuMCzjJjpwiak8VBqia3ZswgLyhOXwf74Q/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "![](https://mmbiz.qpic.cn/mmbiz_png/yjFUicxiaClgWcr3FpZjxq3cZLrVhQf7DA7LibsHvAw1gIZbh2tLibGVFKA2ic9UomjkEJib04zTicDhmny6d6ibXycQZg/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0    12\n1    16\n2     9\n3    12\n4    13\n5    15\ndtype: int32\n0    NaN\n1    4.0\n2   -7.0\n3    3.0\n4    1.0\n5    2.0\ndtype: float64\n1    16\n5    15\n4    13\n3    12\n0    12\n2     9\ndtype: int32\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[8, 12, 9, 10, 13, 15, 14, 11]"
      ]
     },
     "metadata": {},
     "execution_count": 71
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "np.random.seed(112)\n",
    "x = pd.Series(np.random.randint(8,18,6))\n",
    "print(x)\n",
    "# 对x中的元素做一阶差分\n",
    "print(x.diff())\n",
    "\n",
    "# 对x中的元素做降序处理\n",
    "print(x.sort_values(ascending = False))\n",
    "\n",
    "y = pd.Series(np.random.randint(8,16,100))\n",
    "# 将y中的元素做排重处理，并转换为列表对象\n",
    "y.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{0: {0: 0, 1: 4, 2: 8},\n",
       " 1: {0: 1, 1: 5, 2: 9},\n",
       " 2: {0: 2.0, 1: nan, 2: 10.0},\n",
       " 3: {0: 3, 1: 7, 2: 11}}"
      ]
     },
     "metadata": {},
     "execution_count": 107
    }
   ],
   "source": [
    "s.to_dict()"
   ]
  }
 ]
}